Project Aim¶
Since I want to develop myself in text mining and do not know much about it, I wanted to select interesting data which has already studied on Kaggle.
I want to classify Kaggle ex featured prediction competition data which is “Toxic Comment Classification Challenge”. This competition’s aim is to find negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful or otherwise likely to make someone leave a discussion). In this competition, I am going to challenge to build a multi-headed classifying model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate. I combine them with one column and try to find comments which contain any of them.
You can reach competition page at the link below.
https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge
Data¶
I will be using a dataset of comments from Wikipedia’s talk page edits which is provided by Kaggle for competition. You can reach data at the link below.
https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
Download Data and Import Necessary Libraries¶
In [64]:
import time
import warnings
import pandas as pd, numpy as np
%matplotlib inline
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
#color = sns.color_palette()
#from wordcloud import WordCloud ,STOPWORDS
#from PIL import Image
import re
from nltk.tokenize import TweetTokenizer
from nltk.stem.wordnet import WordNetLemmatizer
color = sns.color_palette()
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
#import locale;
#print(locale.getdefaultlocale());
from IPython.display import Image
from IPython.core.display import HTML
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from scipy.sparse import hstack
nltk.download('wordnet')
nltk.download('stopwords')
eng_stopwords = set(stopwords.words("english"))
warnings.filterwarnings("ignore")
tokenizer=TweetTokenizer()
lem = WordNetLemmatizer()
df = pd.read_csv('/Users/yetkineser/Desktop/BDA 502/project/data/train.csv')
df = shuffle(df,random_state=7)
df_others = df.iloc[20000:,]
df = df.iloc[:20000,]
df = df.reset_index(drop=True)
df_others = df_others.reset_index(drop=True)
Inside of Data (EDA)¶
In [65]:
df.head(15)
Out[65]:
- Couple of comments
In [66]:
df['comment_text'][3]
Out[66]:
In [67]:
df['comment_text'][7]
Out[67]:
- The mean, standard deviation and max lengths of comments
In [68]:
lengths = df.comment_text.str.len()
lengths.mean(), lengths.std(), lengths.max()
Out[68]:
- Histogram of comments lengths
In [69]:
lengths.hist();
- I will create one label to predict which is "any". It will be 1 if any of "toxic", "severe toxic", "obscene", "threat", "insult" or "identity_hate" columns are 1.
In [70]:
label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
df['none'] = 1-df[label_cols].max(axis=1)
df['any'] = df[label_cols].max(axis=1)
df.describe()
Out[70]:
- Number of toxic comments we have.
In [71]:
label_cols = ['any','toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
for col in label_cols:
count = df.groupby(col)['any'].count()
new_df = pd.concat([count], axis=1)
new_df.columns = ['count']
display(new_df.sort_values(by=['count'],ascending=False))
In [72]:
print("Total comments = ",len(df))
print("Total clean comments = ",len(df)-df['any'].sum())
- Looking visually have many comments in which category.
In [73]:
x=df.iloc[:,2:10].sum()
#plot
plt.figure(figsize=(8,4))
ax= sns.barplot(x.index, x.values, alpha=0.8)
plt.title("# per class")
plt.ylabel('# of Occurrences', fontsize=12)
plt.xlabel('Type ', fontsize=12)
#adding the text labels
rects = ax.patches
labels = x.values
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')
plt.show()
- Looking visually how many commnets has have have many category.
In [74]:
rowsums=df.iloc[:,2:].sum(axis=1)
x=rowsums.value_counts()
#plot
plt.figure(figsize=(8,4))
ax = sns.barplot(x.index, x.values, alpha=0.8,color=color[2])
plt.title("Multiple tags per comment")
plt.ylabel('# of Occurrences', fontsize=12)
plt.xlabel('# of tags ', fontsize=12)
#adding the text labels
rects = ax.patches
labels = x.values
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')
plt.show()
- Check to null values on dataset.
In [75]:
print("Check for missing values in Train dataset")
null_check=df.isnull().sum()
print(null_check)
There are not null values in our dataset.
Looking first five rows in our dataset
In [76]:
df.head()
Out[76]:
- Looking correlation between tags of comments.
In [77]:
temp_df=df.iloc[:,2:-3]
# filter temp by removing clean comments
# temp_df=temp_df[~train.clean]
corr=temp_df.corr()
plt.figure(figsize=(10,8))
sns.heatmap(corr,
xticklabels=corr.columns.values,
yticklabels=corr.columns.values, annot=True)
Out[77]:
- Most corelated categories are obscene and insults.
Divide Train and Test Dataset and Cross Validation¶
In [105]:
PATH = "/Users/yetkineser/Desktop/BDA 502/project/photos/"
Image(filename = PATH + "crossvalidation.png", width=800, height=600)
Out[105]:
- My project has two steps firstly. But i decided to add new two steps later.
- In first steps i divide my data train and test. And on train set I use 5 fold cross validation. After i learn my model on train dataset and predict on test dataset.
- In second step, i multiply 10 times my target = 1 data in train dataset and add train.
In [79]:
total_rows = (len(df))
train_rows = round(0.8*total_rows)
train = df.iloc[:train_rows,]
train_others = train # i added this later
test = df.iloc[train_rows-total_rows:,]
train_2=train.iloc[:,0:2]
test_2=test.iloc[:,0:2]
df_2=df.iloc[:,0:2]
df_2=df_2.reset_index(drop=True)
print("- I have ",total_rows, " rows on my data set ")
print("- I have ",train_rows, " rows on my first train set ")
print("- I have ",total_rows-train_rows, " rows on my test set ")
- I use k-fold cross validation here.
- Cross Validation is a techniques for validate your algorithms more than one time.
- Thanks to cross validation you can choose best ML algorithms and best features for your data after test it k times.
- You can test robustness k times.
- You can find more about cross validation at the links below .
Cleaning Comments¶
- Create Aphost dictionary for change contraction.
In [80]:
#https://drive.google.com/file/d/0B1yuv8YaUVlZZ1RzMFJmc1ZsQmM/view
# Aphost lookup dict
APPO = {
"aren't" : "are not",
"can't" : "can not",
"couldn't" : "could not",
"didn't" : "did not",
"doesn't" : "does not",
"don't" : "do not",
"hadn't" : "had not",
"hasn't" : "has not",
"haven't" : "have not",
"he'd" : "he would",
"he'll" : "he will",
"he's" : "he is",
"i'd" : "I would",
"i'd" : "I had",
"i'll" : "I will",
"i'm" : "I am",
"isn't" : "is not",
"it's" : "it is",
"it'll":"it will",
"i've" : "I have",
"let's" : "let us",
"mightn't" : "might not",
"mustn't" : "must not",
"shan't" : "shall not",
"she'd" : "she would",
"she'll" : "she will",
"she's" : "she is",
"shouldn't" : "should not",
"that's" : "that is",
"there's" : "there is",
"they'd" : "they would",
"they'll" : "they will",
"they're" : "they are",
"they've" : "they have",
"we'd" : "we would",
"we're" : "we are",
"weren't" : "were not",
"we've" : "we have",
"what'll" : "what will",
"what're" : "what are",
"what's" : "what is",
"what've" : "what have",
"where's" : "where is",
"who'd" : "who would",
"who'll" : "who will",
"who're" : "who are",
"who's" : "who is",
"who've" : "who have",
"won't" : "will not",
"wouldn't" : "would not",
"you'd" : "you would",
"you'll" : "you will",
"you're" : "you are",
"you've" : "you have",
"'re": " are",
"wasn't": "was not",
"we'll":" will",
"didn't": "did not",
"tryin'":"trying"
}
In [81]:
corpus=df_2.comment_text
train_text = train_2.comment_text
test_text = test_2.comment_text
- Create clean functions to clean comments.
- lowercasing,
- remove leaky elements (like ip, users)
- remove usernames
- split comments into words.
- remove numbers
In [82]:
def clean(comment):
"""
This function receives comments and returns clean word-list
"""
#Convert to lower case , so that Hi and hi are the same
comment=comment.lower()
#remove \n
comment=re.sub("\\n"," ",comment)
comment=re.sub("/"," ",comment) # added
comment=re.sub("•"," ",comment) # added
# remove leaky elements like ip,user
comment=re.sub("\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}","",comment)
#removing usernames
comment=re.sub("\[\[.*\]","",comment)
#Split the sentences into words
words=tokenizer.tokenize(comment)
# (')aphostophe replacement (ie) you're --> you are
# ( basic dictionary lookup : master dictionary present in a hidden block of code)
words=[APPO[word] if word in APPO else word for word in words]
words=[lem.lemmatize(word, "v") for word in words]
words = [w for w in words if not w in eng_stopwords]
clean_sent=" ".join(words)
# remove any non alphanum,digit character
clean_sent=re.sub("\W+"," ",clean_sent)
clean_sent=re.sub(" "," ",clean_sent)
clean_sent=re.sub(r'[0-9]+', '', clean_sent)
return(clean_sent)
- One of comment before cleaning
In [83]:
corpus.iloc[5]
Out[83]:
- Same comment after cleaning
In [84]:
clean(corpus.iloc[5])
Out[84]:
In [85]:
clean_corpus=corpus.apply(lambda x :clean(x))
TF(Term Frequency) - IDF(Inverse Document Frequency)¶
- Tf-Idf is a feature extraction techniques for text. Tf is looking word (or char) frequency in row. Idf is looking frequency in all documents.
- You can find more detail about tf-idf at the links below.
In [86]:
def top_feats_by_class(Xtr, y, features, min_tfidf=0.1, top_n=25):
''' Return a list of dfs, where each df holds top_n features and their mean tfidf value
calculated across documents with the same class label. '''
dfs = []
labels = np.unique(y)
for label in labels:
ids = np.where(y==label)
feats_df = top_mean_feats(Xtr, features, ids, min_tfidf=min_tfidf, top_n=top_n)
feats_df.label = label
dfs.append(feats_df)
return dfs
In [87]:
tfv = TfidfVectorizer(min_df=200, max_features=10000,
strip_accents='unicode', analyzer='word',ngram_range=(1,1),
use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = 'english')
tfv.fit(clean_corpus)
features = np.array(tfv.get_feature_names())
df_unigrams = tfv.transform(clean_corpus.iloc[:df.shape[0]])
In [88]:
#serperate train and test features
df_feats=df.iloc[0:len(df),]
#join the tags
df_tags=df.iloc[:,2:]
df_feats=pd.concat([df_feats,df_tags],axis=1)
In [89]:
def top_tfidf_feats(row, features, top_n=50):
''' Get top n tfidf values in row and return them with their corresponding feature names.'''
topn_ids = np.argsort(row)[::-1][:top_n]
top_feats = [(features[i], row[i]) for i in topn_ids]
df = pd.DataFrame(top_feats)
df.columns = ['feature', 'tfidf']
return df
def top_feats_in_doc(Xtr, features, row_id, top_n=50):
''' Top tfidf features in specific document (matrix row) '''
row = np.squeeze(Xtr[row_id].toarray())
return top_tfidf_feats(row, features, top_n)
def top_mean_feats(Xtr, features, grp_ids, min_tfidf=0.1, top_n=50):
''' Return the top n features that on average are most important amongst documents in rows
indentified by indices in grp_ids. '''
D = Xtr[grp_ids].toarray()
D[D < min_tfidf] = 0
tfidf_means = np.mean(D, axis=0)
return top_tfidf_feats(tfidf_means, features, top_n)
# modified for multilabel milticlass
def top_feats_by_class(Xtr, features, min_tfidf=0.005, top_n=50):
''' Return a list of dfs, where each df holds top_n features and their mean tfidf value
calculated across documents with the same class label. '''
dfs = []
cols=df_tags.columns
for col in cols:
ids = df_tags.index[df_tags[col]==1]
feats_df = top_mean_feats(Xtr, features, ids, min_tfidf=min_tfidf, top_n=top_n)
feats_df.label = label
dfs.append(feats_df)
return dfs
In [90]:
tfidf_top_n_per_lass=top_feats_by_class(df_unigrams,features)
In [91]:
from sklearn.feature_extraction.text import TfidfVectorizer
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
stop_words='english',
ngram_range=(1, 1),
max_features=10000)
word_vectorizer.fit(corpus)
train_word_features = word_vectorizer.transform(train_text)
test_word_features = word_vectorizer.transform(test_text)
char_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='char',
stop_words='english',
ngram_range=(2, 6),
max_features=50000)
char_vectorizer.fit(corpus)
train_char_features = char_vectorizer.transform(train_text)
test_char_features = char_vectorizer.transform(test_text)
Metrics Which I Used in This Project¶
- I used four different metrics which are
- Accuracy
- Precision
- Recall
- AUC(Area Under the Curve)
- You can find definition of accuracy, recall and precision at the table below.
In [94]:
Image(filename = PATH + "metrics.png", width=700, height=700)
Out[94]:
- AOC is calculated with sensitivity(True positive rate) and specifity(False Negative rate), it is a very good metrics to calculate how good we ordered probabilities of our classification.
- You can find about AUC at the link below
- In every study, different metrics can be important in this study i try to find toxic comments. So if i can not find one of toxic comments, it can be problem. So for this study recall is my most important metrics. Second important one is AOC. Of course the other metrics are important. But most important one is recall and AOC.
- Also i compare working time of algorithms in this project.
Logistic Regression¶
- Logistic regression is a regression techniques which is used classification.
- Logistic regression is changed from regression with some formulas.
- You can find more about logistic regression on below links.
- https://www.datacamp.com/community/tutorials/logistic-regression-R?utm_source=adwords_ppc&utm_campaignid=1001535064&utm_adgroupid=48949243189&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=1t1&utm_creative=236326164852&utm_targetid=dsa-379899700955&utm_loc_interest_ms=&utm_loc_physical_ms=1012782&gclid=EAIaIQobChMIldfM86G12wIVRWcZCh3-Qw3WEAAYASAAEgKF6fD_BwE
- http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
In [95]:
Image(filename = PATH + "logistic regression.png", width=700, height=600)
Out[95]:
- There are so many paramaters in sklearn logistic regression packages, after few tries with cross validation, i have decided to use C=5 and solver = 'sag' with other default parameters.
In [96]:
from sklearn.linear_model import LogisticRegression
class_names = ['any']
from sklearn.model_selection import cross_val_score
from scipy.sparse import hstack
train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])
scores = []
scores2 = []
scores3 = []
test_pred = pd.DataFrame.from_dict({'id': test['id']})
train_pred = pd.DataFrame.from_dict({'id': train['id']})
time1=time.time()
for class_name in class_names:
train_target = train[class_name]
classifier = LogisticRegression(C=5, solver='sag')
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='accuracy')
scores.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='precision')
scores2.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='recall')
scores3.append(cv_score)
time2=time.time()
print('Average CV accuracy is {}'.format(round(np.mean(scores),5)))
print('Standard Deviation of CV accuracy is {}'.format(round(np.std(scores),5)))
print('Average CV precion is {}'.format(round(np.mean(scores2),5)))
print('Standard Deviation of CV precision is {}'.format(round(np.std(scores2),5)))
print('Average CV recall is {}'.format(round(np.mean(scores3),5)))
print('Standard Deviation of CV recall is {}'.format(round(np.std(scores3),5)))
print("Time of cross validation",round(time2-time1,5))
In [98]:
classifier = LogisticRegression(C=5, solver='sag')
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
In [99]:
test["pred"]=test_pred["any"]>0.5
train["pred"]=train_pred["any"]>0.5
print("Accuracy score of train set : ",round(accuracy_score(train["any"], train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(train["any"], train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(train["any"], train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [100]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
- Confusion Matrix of Train set
In [141]:
confusion_matrix_train = confusion_matrix(train["any"], train["pred"])
print(confusion_matrix_train)
- Confusion Matrix of Test set
In [142]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
Naive Bayes Classifier¶
- It is a simple ml classifying method based on basic statistict. It generally use on big datasets and use for its fastness. Do not use generally for high metrics.
- You can find mora about Naive Bayes at the links below.
In [143]:
class_names = ['any']
train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])
scores = []
scores2 = []
scores3 = []
test_pred = pd.DataFrame.from_dict({'id': test['id']})
train_pred = pd.DataFrame.from_dict({'id': train['id']})
time1=time.time()
for class_name in class_names:
train_target = train[class_name]
classifier = MultinomialNB()
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='accuracy')
scores.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='precision')
scores2.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='recall')
scores3.append(cv_score)
time2=time.time()
print('Average CV accuracy is {}'.format(round(np.mean(scores),5)))
print('Standard Deviation of CV accuracy is {}'.format(round(np.std(scores),5)))
print('Average CV precion is {}'.format(round(np.mean(scores2),5)))
print('Standard Deviation of CV precision is {}'.format(round(np.std(scores2),5)))
print('Average CV recall is {}'.format(round(np.mean(scores3),5)))
print('Standard Deviation of CV recall is {}'.format(round(np.std(scores3),5)))
print("Time of cross validation",round(time2-time1,5))
In [207]:
classifier = MultinomialNB()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
In [208]:
test["pred"]=test_pred["any"]>0.5
train["pred"]=train_pred["any"]>0.5
print("Accuracy score of train set : ",round(accuracy_score(train["any"], train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(train["any"], train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(train["any"], train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [209]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
- Confusion Matrix of Train set
In [210]:
confusion_matrix_train = confusion_matrix(train["any"], train["pred"])
print(confusion_matrix_train)
- Confusion Matrix of Test set
In [211]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
AdaBoost Classifier¶
- Adaboost is one of the ensemble machine learning methods. It generally use for high predicted power. But it works slowly compared to naive bayes and logistic regression.
In [132]:
class_names = ['any']
train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])
scores = []
scores2 = []
scores3 = []
test_pred = pd.DataFrame.from_dict({'id': test['id']})
train_pred = pd.DataFrame.from_dict({'id': train['id']})
time1=time.time()
for class_name in class_names:
train_target = train[class_name]
classifier = AdaBoostClassifier()
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='accuracy')
scores.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='precision')
scores2.append(cv_score)
cv_score = cross_val_score(classifier, train_features, train_target, cv=5, scoring='recall')
scores3.append(cv_score)
time2=time.time()
print('Average CV accuracy is {}'.format(round(np.mean(scores),5)))
print('Standard Deviation of CV accuracy is {}'.format(round(np.std(scores),5)))
print('Average CV precion is {}'.format(round(np.mean(scores2),5)))
print('Standard Deviation of CV precision is {}'.format(round(np.std(scores2),5)))
print('Average CV recall is {}'.format(round(np.mean(scores3),5)))
print('Standard Deviation of CV recall is {}'.format(round(np.std(scores3),5)))
print("Time of cross validation",round(time2-time1,5))
In [212]:
classifier = AdaBoostClassifier()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
In [213]:
test["pred"]=test_pred["any"]>0.5
train["pred"]=train_pred["any"]>0.5
print("Accuracy score of train set : ",round(accuracy_score(train["any"], train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(train["any"], train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(train["any"], train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [214]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
- Confusion Matrix of Train set
In [215]:
confusion_matrix_train = confusion_matrix(train["any"], train["pred"])
print(confusion_matrix_train)
- Confusion Matrix of Test set
In [216]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
Cross Validations and Trains&Tests Results¶
In [62]:
Image(filename = PATH + "metricsresults1.png", width=800, height=600)
Out[62]:
- Based on working time Naive Bayes is fastest. And based on training time Adaboost is more than 300 times slower from Naive Bayes and 30 times slower than logistic regression. Based on test time Logistic Regression is faster than Naive Bayes.
- Based on cv and test results best algorithms is logistic regression for recall metric(little difference with Adaboost) and AOC metric. But we can say 65% recall is so low.
- Accuracy metrics are so good for all of them but our train dataset seperation is 10%-90% so if i say all results = 0, my accuracy still be 0.90.
- Based on logistic regression, i look some of False Negative(FN) Results. Because if i can decrease FN number, my recall will be increase.
In [101]:
test_recall = test.loc[(test['any'] == 1) & (test['pred']==False)]
test_recall.ix[:, ["comment_text","any","pred"]]
test_recall = test_recall.reset_index(drop=True)
train_recall = train.loc[(train['any'] == 1) & (train['pred']==False)]
In [102]:
test_recall["comment_text"][7]
Out[102]:
In [103]:
test_recall["comment_text"][3]
Out[103]:
In [104]:
test_recall["comment_text"][12]
Out[104]:
- After looking these comments, i thought my train dataset has not enough tagged(any = 1) comments. So i decided to add new two steps to my work.
New Two Steps for Increasing Recall Metrics¶
In [106]:
Image(filename = PATH + "steps2.png", width=800, height=600)
Out[106]:
- In third step i multiply 10 times my tagged data in train dataset and add again my train dataset.
- In fourth step i add completely new dataset on the my train dataset.
- I did not change anything on my test dataset. In 2,3 and 4 steps my test dataset is same. So, I can compare test results.
Step 3 : Added 10 Times Same Tagged Data on My Train Dataset¶
- Finding tagged data on my train dataset.
In [220]:
train_any_1 = train.loc[(train['any'] == 1)]
train_any_1.ix[:, ["comment_text","any","pred"]]
train_any_1 = train_any_1.reset_index(drop=True)
- Adding 10 times.
In [222]:
new_train = pd.concat([train, train_any_1, train_any_1, train_any_1,
train_any_1, train_any_1, train_any_1,
train_any_1,train_any_1, train_any_1, train_any_1])
In [223]:
train_rows = (len(new_train))
test_rows = (len(test))
train_rows = round(train_rows)
train = df.iloc[:train_rows,]
train_2=new_train.iloc[:,0:2]
test_2=test.iloc[:,0:2]
df = pd.concat([train_2,test_2])
df_2=df.iloc[:,0:2]
df_2=df_2.reset_index(drop=True)
print("- I have ",train_rows, " rows on my new regenerated train set ")
print("- I have ",test_rows, " rows on my test set ")
In [224]:
corpus=df_2.comment_text
train_text = train_2.comment_text
test_text = test_2.comment_text
- I clean my corpus again with functions i write above.
In [225]:
clean_corpus=corpus.apply(lambda x :clean(x))
In [226]:
tfv = TfidfVectorizer(min_df=200, max_features=10000,
strip_accents='unicode', analyzer='word',ngram_range=(1,1),
use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = 'english')
tfv.fit(clean_corpus)
features = np.array(tfv.get_feature_names())
df_unigrams = tfv.transform(clean_corpus.iloc[:df.shape[0]])
In [227]:
#serperate train and test features
df_feats=df.iloc[0:len(df),]
#join the tags
df_tags=df.iloc[:,2:]
df_feats=pd.concat([df_feats,df_tags],axis=1)
In [228]:
tfidf_top_n_per_lass=top_feats_by_class(df_unigrams,features)
In [229]:
from sklearn.feature_extraction.text import TfidfVectorizer
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
stop_words='english',
ngram_range=(1, 1),
max_features=10000)
word_vectorizer.fit(corpus)
train_word_features = word_vectorizer.transform(train_text)
test_word_features = word_vectorizer.transform(test_text)
char_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='char',
stop_words='english',
ngram_range=(2, 6),
max_features=50000)
char_vectorizer.fit(corpus)
train_char_features = char_vectorizer.transform(train_text)
test_char_features = char_vectorizer.transform(test_text)
Logistic Regression with Regenerated Data¶
In [230]:
train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])
train_target = new_train[class_name]
test_pred = pd.DataFrame.from_dict({'id': test['id']})
train_pred = pd.DataFrame.from_dict({'id': new_train['id']})
classifier = LogisticRegression(C=5, solver='sag')
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train["any"], new_train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train["any"], new_train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train["any"], new_train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [232]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
In [233]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
Naive Bayes Classifier with Regenerated Data¶
In [234]:
classifier = MultinomialNB()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train["any"], new_train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train["any"], new_train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train["any"], new_train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [236]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
In [237]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
AdaBoost Classifier with Regenerated Data¶
In [238]:
classifier = AdaBoostClassifier()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train["any"], new_train["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train["any"], new_train["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train["any"], new_train["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [239]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
In [240]:
confusion_matrix_test = confusion_matrix(test["any"], test["pred"])
print(confusion_matrix_test)
Step 2 and Step 3 Results¶
In [112]:
Image(filename = PATH + "results2.png", width=800, height=600)
Out[112]:
- After added same dataset on our train dataset our accuracy, precision, AUC(except Naive Bayes) are decrease. But our recall score are increase especially in Naive Bayes.
- Based on Recall Score our best Algorithm is Naive Bayes based on second step test results.
Step 4 : Added New Tagged Data on My Train Dataset¶
- I add new data and apply same cleaning and nlp process on them.
In [108]:
len(df_others)
label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
df_others['none'] = 1-df_others[label_cols].max(axis=1)
df_others['any'] = df_others[label_cols].max(axis=1)
In [242]:
df_others_any_1 = df_others.loc[(df_others['any'] == 1)]
df_others_any_1.ix[:, ["comment_text","any","pred"]]
df_others_any_1 = df_others_any_1.reset_index(drop=True)
In [243]:
len(df_others_any_1)
Out[243]:
In [244]:
new_train_2 = pd.concat([train_others, df_others_any_1])
len(new_train_2)
Out[244]:
In [245]:
train_rows = (len(new_train_2))
test_rows = (len(test))
train_rows = round(train_rows)
train = df.iloc[:train_rows,]
train_2=new_train_2.iloc[:,0:2]
test_2=test.iloc[:,0:2]
df = pd.concat([train_2,test_2])
df_2=df.iloc[:,0:2]
df_2=df_2.reset_index(drop=True)
print("- I have ",train_rows, " rows on my new regenerated train set ")
print("- I have ",test_rows, " rows on my test set ")
In [246]:
corpus=df_2.comment_text
train_text = train_2.comment_text
test_text = test_2.comment_text
clean_corpus=corpus.apply(lambda x :clean(x))
tfv = TfidfVectorizer(min_df=200, max_features=10000,
strip_accents='unicode', analyzer='word',ngram_range=(1,1),
use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = 'english')
tfv.fit(clean_corpus)
features = np.array(tfv.get_feature_names())
df_unigrams = tfv.transform(clean_corpus.iloc[:df.shape[0]])
#serperate train and test features
df_feats=df.iloc[0:len(df),]
#join the tags
df_tags=df.iloc[:,2:]
df_feats=pd.concat([df_feats,df_tags],axis=1)
tfidf_top_n_per_lass=top_feats_by_class(df_unigrams,features)
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='word',
token_pattern=r'\w{1,}',
stop_words='english',
ngram_range=(1, 1),
max_features=10000)
word_vectorizer.fit(corpus)
train_word_features = word_vectorizer.transform(train_text)
test_word_features = word_vectorizer.transform(test_text)
char_vectorizer = TfidfVectorizer(
sublinear_tf=True,
strip_accents='unicode',
analyzer='char',
stop_words='english',
ngram_range=(2, 6),
max_features=50000)
char_vectorizer.fit(corpus)
train_char_features = char_vectorizer.transform(train_text)
test_char_features = char_vectorizer.transform(test_text)
Logistic Regression with Addition of New Any = 1 Dataset¶
In [247]:
train_features = hstack([train_char_features, train_word_features])
test_features = hstack([test_char_features, test_word_features])
train_target = new_train_2[class_name]
test_pred = pd.DataFrame.from_dict({'id': test['id']})
train_pred = pd.DataFrame.from_dict({'id': new_train_2['id']})
classifier = LogisticRegression(C=5, solver='sag')
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train_2["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train_2["any"], new_train_2["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train_2["any"], new_train_2["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train_2["any"], new_train_2["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [248]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train_2["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
Naive Bayes Classifier with Addition of New Any = 1 Dataset¶
In [249]:
classifier = MultinomialNB()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train_2["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train_2["any"], new_train_2["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train_2["any"], new_train_2["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train_2["any"], new_train_2["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [250]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train_2["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
AdaBoost Classifier with Addition of New Any = 1 Dataset¶
In [251]:
classifier = AdaBoostClassifier()
time1=time.time()
classifier.fit(train_features, train_target)
time2=time.time()
train_pred[class_name] = classifier.predict_proba(train_features)[:, 1]
time3=time.time()
test_pred[class_name] = classifier.predict_proba(test_features)[:, 1]
time4=time.time()
test["pred"]=test_pred["any"]>0.5
new_train_2["pred"]=train_pred["any"]>0.5
print("Training time = ",round(time2-time1,5))
print("Test time = ",round(time4-time3,5))
print("Accuracy score of train set : ",round(accuracy_score(new_train_2["any"], new_train_2["pred"]),5))
print("Accuracy score of test set : ",round(accuracy_score(test["any"], test["pred"]),5))
print("Precision score of train set : ",round(precision_score(new_train_2["any"], new_train_2["pred"]),5))
print("Precision score of test set : ",round(precision_score(test["any"], test["pred"]),5))
print("Recall score of train set : ",round(recall_score(new_train_2["any"], new_train_2["pred"]),5))
print("Recall score of test set : ",round(recall_score(test["any"], test["pred"]),5))
In [252]:
# calculate the fpr and tpr for all thresholds of the classification
from sklearn import metrics
preds = test["pred"]
fpr, tpr, threshold = metrics.roc_curve(test["any"], test_pred["any"])
fpr_t, tpr_t, threshold_t = metrics.roc_curve(new_train_2["any"], train_pred["any"])
roc_auc = metrics.auc(fpr, tpr)
roc_auc_t = metrics.auc(fpr_t, tpr_t)
# method I: plt
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'TEST_AUC = %0.5f' % roc_auc)
plt.plot(fpr_t, tpr_t, 'r', label = 'TRAIN_AUC = %0.5f' % roc_auc_t)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
Step 2, 3 and 4 Results¶
In [114]:
Image(filename = PATH + "results3.png", width=800, height=600)
Out[114]:
- Based on the steps 4 test results best algorithms is Logistic Regression for all metrics.
- So we can say logistic regression can not learn repeated data very effectively but it can learn new data really good.
- Also when we added new data our precision and accuracy score are decreased.
- But our recall, AOC and gini coefficients (2*AOC-1) are increased.
- Result of step 2 and step 3 we can say, data manipulation on our train dataset can give effective results on out test dataset metrics. Also it give different size of effects for all algorithms(For example Logistic regression can not learn repeated data compared the other two).
- Result of step 2 and step 4 we can say, adding new data on train dataset can change test dataset metrics in a good way or bad way.
- Also Cause of the accuracy decrease of step 3,4 test set, their test and train dataset data distribution are so different.